Integrated simulation platform for conventional, connected and automated driving: A design from cyber–physical systems perspective

A comprehensive assessment of connected and automated driving is imperative before its large-scale deployment in reality, which can be economically and effectively implemented via a credible simulation platform. Nonetheless, the key components of traffic dynamics, vehicle modeling, and traffic environment are oversimplified in existing simulators. Current traffic simulators normally simplify the function of connected and autonomous vehicles by proposing incremental improvements to the conventional traffic flow modeling methods, which cannot reflect the characteristics of the realistic connected and autonomous vehicles. On the other hand, typical autonomous vehicle simulators only focus on individual function verification in some specific traffic scenarios, omitting the network-level evaluation by integrating both large-scale traffic networks and vehicle-to-anything (V2X) communication. This paper designs a comprehensive simulation platform for conventional, connected and automated driving from a transportation cyber–physical system perspective, which tightly combines the core components of V2X communication, traffic networks, and autonomous/conventional vehicle model. Specifically, three popular open-source simulators SUMO, Omnet++, and Webots are integrated and connected via the traffic control interface, and the whole simulation platform will be deployed in a Client/Server model. As the demonstration, two typical applications, traffic flow optimization and vehicle eco-driving, are implemented in the simulation platform. The proposed platform provides an ideal and credible testbed to explore the potential social/economic impact of connected and automated driving from the individual level to the large-scale network level.


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01767684
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 21 2021 3:31PM